Alignment Exists Because ML Never Became a Science

One reason people are confused about AI alignment losing its ability to make scientific progress is that machine learning as a whole is also not a very scientific field by the standard I’m applying. Even most early AI researchers were more focused on building artificial intelligence than on understanding scientific principles of cognition. The rise of deep learning exacerbated this problem, as throwing more compute and engineering effort at an AI became arbitrarily scalable. In an important sense, the field of alignment is necessary because the field of ML didn’t prioritize gaining a deep understanding of the systems it was building. (Eliezer makes a similar point in this dialogue.)